Kishi NAOTO (來住 直人)

A high-sensitivity refractive index (RI) sensor is proposed and designed, which is an in-line Mach-Zehnder interferometer (IMZI) with a photonic crystal fiber (PCF) and multilevel micro spindle structures fabricated by the arc-discharging and tapering method. To improve the sensitivity, a tapered multimode fiber (MMF) is inserted into the PCF. The performance of RI and temperature detection has been analyzed and discussed. The maximum RI sensitivity of -2958.875 nm/RIU is achieved by detecting the NaCl solution. In addition, the maximum temperature sensitivity is 6.4 pm/degree in the range of 25 degree - 65 degree, and the maximum measurement error changes caused by temperature is -0.0170 per mille/degree. It shows that the sensor is very sensitive to RI yet insensitive to temperature.

We propose a fiber-based in-line Mach-Zehnder interferometer capable of simultaneous measurement of acoustic signals and temperature. This interferometer employs distinctive peanut-shaped junctions which are fabricated by the discharge function of an optical fiber fusion splicer. With the peanut-shaped junctions, a single-mode optical fiber(SMF), a polarization-maintaining optical fiber(PMF), and another single-mode optical fiber(SMF) are connected to form a SMF-PMF-SMF structure. By using the curvature and temperature dependencies of the sensor's extinction wavelength in the interferometer, we have achieved a highly sensitive acoustic and temperature sensor.

This article proposes a coniferous and broad-leaved forest distinguishing method using L-band polarimetric SAR data based on the structure-orientation parameter. The structure-orientation parameter is one of the averaged Stokes vector-based discriminators which is sensitive to the composition of equivalent horizontal and vertical structures. In the proposed method, the structure-orientation parameters is compensated by employing the scattered power information to remove the influence of the topography. The final distinguishing result is generated based on the statistical feature of the compensated parameters. The experiments using several sets of ALOS2-PALSAR2 level 1.1 data prove that the proposed method has high performance for forest-type distinguishing.